{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn, optim\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 设置设备\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# 如果要再GPU上运行，那么要把相应变量和方法用.to(device)设置，比如说x = x.to(device)\n",
    "\n",
    "# 1. 获取参数\n",
    "x = torch.rand([500, 1])\n",
    "y = 3 * x - 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 创建线性回归类\n",
    "class Lr(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(1, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.linear(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 创建线性回归对象，损失函数，优化器\n",
    "model = Lr()\n",
    "critier = nn.MSELoss()\n",
    "op = optim.SGD(model.parameters(), 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "for i in range(10000):\n",
    "    # 4. 进行预测\n",
    "    y_pre = model(x)\n",
    "    \n",
    "    # 5. 计算损失\n",
    "    loss = critier(y, y_pre)  # 严格上来说第一个参数是真实值，第二个参数是预测值\n",
    "    \n",
    "    # 6. 反向传播\n",
    "    op.zero_grad()  # 梯度归零\n",
    "    loss.backward()  # 计算梯度\n",
    "    # 7. 更新参数\n",
    "    op.step()\n",
    "    \n",
    "#     if i % 500 == 0:\n",
    "#         print(loss.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0873)\n"
     ]
    }
   ],
   "source": [
    "print(loss.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. 可视化\n",
    "model.eval()  # 设置模型为评估模型\n",
    "predict = model(x)  # 计算预测值\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.data.numpy(), y.data.numpy(), c='r')\n",
    "plt.plot(x.data.numpy(), predict.data.numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    lr: 0.001\n",
      "    momentum: 0\n",
      "    nesterov: False\n",
      "    weight_decay: 0\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand([500, 1]).to(device)  # cpu转gpu\n",
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 3 * x + 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Lr(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Linear(1, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.fc(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Lr().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), 0.001)\n",
    "criteria = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    "for i in range(5000):\n",
    "    model.train(mode=True)\n",
    "    optimizer.zero_grad()\n",
    "    y_pre = model(x)\n",
    "    loss = criteria(y_pre, y)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    print(f\"loss:{loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lr(\n",
       "  (fc): Linear(in_features=1, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ca0f9ff308>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = model.cpu()  # gpu转cpu\n",
    "x = x.cpu()\n",
    "y = y.cpu()\n",
    "y_predict = model(x)\n",
    "plt.style.use(\"seaborn-darkgrid\")\n",
    "plt.rc(\"font\", size=20)\n",
    "plt.rc(\"figure\", figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.numpy(), y.numpy(), c='y')\n",
    "plt.plot(x.numpy(), y_predict.data.numpy(), c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
